
What is the individual contribution of each optimization to cost-efficiency? For we perform a number of micro-benchmarks.

Is it possible to achieve better cost-efficiency with an optimized FaaS platform than a VM-based, i.e., “serverful” substrate (IaaS) for distributed ML training? For
we run several ML training jobs of different flavors, including both dense and sparse ML models. We use PyTorch [1]}, a specialized “serverful” ML library, but also
a non-specialized, serverless data-analytics system, to determine what happens when FaaS is not specialized to model training.

Is the ISP consistency model much more effective than other bounded staleness models such as SSP? The goal is to infer what type of synchronization strategy is more appropriate
for the indirect communication model of FaaS cloud platforms.

